{"_canonicalization":{"envelope_id":"axm_ + sha256(envelope minus {signature, axiom_id, anchors})","envelope_signature":"ed25519(envelope minus {signature, axiom_id})","json":"sort_keys=True, separators=(',',':'), ensure_ascii=False, allow_nan=False, utf-8","leaf_hash":"sha256(0x00 || canonical_json(envelope_full))","seal_signature":"ed25519(seal minus {signature, sig_algorithm})"},"axiom_id":"axm_40c40d983216ae005ae479e32854ef2e8296024ebbdebe4443079459a1231a32","bitcoin_anchor":{"bitcoin_attestations":[],"calendar_attestations":[],"ots_url":"","stamped_at":"","status":"pending_next_stamp"},"envelope":{"anchors":[{"chain":"crovia.axiom_graph","height":0,"merkle_proof":"spider_vendor_press_v1","root_at_anchor":"spider_vendor_press_v1"}],"axiom_id":"axm_40c40d983216ae005ae479e32854ef2e8296024ebbdebe4443079459a1231a32","axiom_type":"AX.OBS","body":{"axiom_subtype":"news.vendor_press.v1","category":"news","fingerprint":"abe83b24e4dc5cd460ce5f95bff2aa3971156bbff7a30f525ec92dc54a34f649","published":"Wed, 08 Jul 2026 00:00:00 -0400","receipt_hash":"abe83b24e4dc5cd460ce5f95bff2aa3971156bbff7a30f525ec92dc54a34f649","schema":"spider.news.vendor_press.v1","spider":"vendor_press","spider_record":{"axiom_subtype":"news.vendor_press.v1","category":"news","decision_hint":"POSITIVE","envelope_target":"AX.OBS","fingerprint":"abe83b24e4dc5cd460ce5f95bff2aa3971156bbff7a30f525ec92dc54a34f649","observed_at":"2026-07-08T04:43:57.834712Z","parent_run_hash":"46ab019f0b0f0bfcde5e14ed7c256069c6fa8c8079b9f87fd3a8a6d9259e3864","published":"Wed, 08 Jul 2026 00:00:00 -0400","runtime_version":"0.1.0","schema":"spider.news.vendor_press.v1","source_status":200,"source_url":"https://export.arxiv.org/rss/cs.AI","spider":"vendor_press","summary_excerpt":"arXiv:2607.05943v1 Announce Type: new \nAbstract: Training multimodal search agents to perform multi-hop reasoning remains challenging due to a fundamental structural disconnect: existing pipelines construct training data, search environments, and reward signals independently, causing synthesized structural metadata to be discarded, environments to rely on irreproducible external engines, and RL rewards to remain sparse at the trajectory level. We present \\textbf{SearchEyes}, which uses a typed knowledge graph as the backbone of a \\emph{simulated search world} that unifies all three components. We propose \\textbf{Perception-Knowledge Chains (PKC)} to sample constrained multi-hop paths over the visual-knowledge intersection of Wikidata5M, retaining hop-level entity metadata that simultaneously defines a self-contained search world and step-level reward anchors. We further propose \\textbf{Hop-Anchored Policy Optimization (HaPO)}, which reuses these anchors for step-level credit assignment","title":"SearchEyes: Towards Frontier Multimodal Deep Search Intelligence via Search World Simulation","url":"https://arxiv.org/abs/2607.05943","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.05943v1 Announce Type: new \nAbstract: Training multimodal search agents to perform multi-hop reasoning remains challenging due to a fundamental structural disconnect: existing pipelines construct training data, search environments, and reward signals independently, causing synthesized structural metadata to be discarded, environments to rely on irreproducible external engines, and RL rewards to remain sparse at the trajectory level. We present \\textbf{SearchEyes}, which uses a typed knowledge graph as the backbone of a \\emph{simulated search world} that unifies all three components. We propose \\textbf{Perception-Knowledge Chains (PKC)} to sample constrained multi-hop paths over the visual-knowledge intersection of Wikidata5M, retaining hop-level entity metadata that simultaneously defines a self-contained search world and step-level reward anchors. We further propose \\textbf{Hop-Anchored Policy Optimization (HaPO)}, which reuses these anchors for step-level credit assignment","title":"SearchEyes: Towards Frontier Multimodal Deep Search Intelligence via Search World Simulation","vendor":"arxiv_cs_ai"},"confidence":{"method":"deterministic"},"decision":"POSITIVE","issued_at":"2026-07-08T04:43:57Z","notes":"Spider vendor_press (news) news.vendor_press.v1","object":{"captured_by":"crovia.spider.vendor_press","primary_source_url":"https://arxiv.org/abs/2607.05943"},"predecessors":[],"schema":"crovia.axiom.v1","signature":"ed25519:c4869a7c875ff31f3629da47c6deccab82c6c54c16f3344069840d2f59579f2045e2cae0e0c6bdccc7447eb335f2e178935fdf35019899aba6f150eb52132909","signer":"crovia.substrate","subject":{"observed_at":"2026-07-08T04:43:57Z","source_collector":"spider:vendor_press","target_id":"https://arxiv.org/abs/2607.05943"},"tsa":{"authority":"crovia.substrate.bootstrap","rfc3161_token":"{\"kind\":\"crovia.bootstrap.tsa\",\"source_jsonl\":\"/opt/crovia/spider/data/news/vendor_press_v1.jsonl\",\"source_seal_merkle_root\":\"spider_vendor_press_v1\",\"upgrade_path\":\"Sessione H \\u2014 OpenTimestamps weekly anchor\"}"},"zk_mode":"clear","zk_proof":null},"ledger":{"leaf_hash":"80bcbac460de4337e9dd4cc21f9c7ef7959df029a511bd8ccfdb07f6b6741aad","leaf_index":292628,"ledger_path":"/opt/crovia/substrate/axiom_ledger.jsonl"},"merkle_proof":{"hash_alg":"sha256","leaf_prefix":"0x00","node_prefix":"0x01","odd_leaf_rule":"duplicate_last","path":[{"sibling":"0e476bbc953385e234e43fdab2958b9f49ddb5597632602efa38d4147f2cb1b3","side":"right"},{"sibling":"93770ff1480cf83a458118e266c8f94650fa813fcda703924ca58dab30e8f6ab","side":"right"},{"sibling":"80d541485628e711756074dfef18fc7f24fc026cec11917f988fbded6799c9e8","side":"left"},{"sibling":"fe783087319d71857993ae5d84d47b44c7dc452516d829bdbbf171469b478fdc","side":"right"},{"sibling":"b58ade3a2b4d2d1308013db14f36ff3d331b5999484f8b28ab0610645d1adc64","side":"left"},{"sibling":"268657446c37b3e0150999dbf0995a3121d979a7d47b0e126f3fdcd6c05982a9","side":"right"},{"sibling":"28bb59643b308594ed3d4dae811cdf7adef9eb3ec914130dd008ce6291093259","side":"right"},{"sibling":"7db888cb3645d8b5e3052bd5d092a85490e29ab732a691eb6a8b8560770aa0ff","side":"right"},{"sibling":"1515812edbf9903d3d787f20218b8577e2f1fef32592508ce01a3b3ebe75f507","side":"left"},{"sibling":"d4b475beae71e5b4d5ab7e66f7144e7b8e1356fcd32339e49df234b455659fef","side":"left"},{"sibling":"cead64e0790e8871aeea2334210146b5e35fe7e4bc10748acdf76da0c565be05","side":"left"},{"sibling":"d1231ac6e6bd7d6867a9109fbdadede0b1631e97866ddde70f7ac2d52c28e15f","side":"right"},{"sibling":"76855b4804c75c52bf97aa34358950d42d6103cdc1a86be5f0a2c8de4d65c106","side":"left"},{"sibling":"a75ab4319e241beeddb1b3f5705febe0422937926c3479923ccfb0b0082fa4e3","side":"left"},{"sibling":"bd04fa605f883bfb2b81510d045b1e85e555a03da3be083619f61384dfe40ff8","side":"left"},{"sibling":"9e75f2ab0ddf2dc9e92af7049244c21b909734ab57906a35dfad2853ca9966e2","side":"right"},{"sibling":"e6cd4cad39a4b6ca6647d1b0ad2db86e57e5fa6240f65966c10093e91140769b","side":"right"},{"sibling":"90a7efc6b94ec8913fbdf03f4927a821b9fb89921d526716f5ee28f015303779","side":"right"},{"sibling":"1cecb7f447febd025aac272837c80de218aecc6485d2395a509b2a1f1b9c746e","side":"left"}]},"schema":"crovia.axiom_proof.v1","seal":{"first_collector_run_id":"","first_receipt_hash":"","jsonl_path":"/opt/crovia/substrate/axiom_ledger.jsonl","key_id":"430895f101d38164","last_collector_run_id":"","last_receipt_hash":"","leaf_count":292998,"merkle_root":"f533b7efebdfd8fb6ba3e7cc158ee55261fd53a7985f234cfea359170dad4d5a","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","run_id":"hourly_json_retrofit_20260708T053701Z","schema":"crovia.seal.v1","seal_family_version":"crovia-seal-family/1","seal_kind":"substrate_batch","sealed_at":"2026-07-08T05:38:17Z","sig_algorithm":"ed25519","signature":"07ebb10c732bbffb28b55a5db01d5525f6b8ab7ef36e1e0a68c35c96ded99f7040c277edba75eb6b15c77feda30bd5321e31ada6f572b674d06f4f8e24bd2f07","signer_version":"1.1.0"},"trust_root":{"key_id":"430895f101d38164","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","signature_algorithm":"ed25519","url":"/registry/canon/TRUST_ROOT.md"},"verifier":{"spec":"/registry/canon/AXIOM_RECEIPT_v1.md","url":"/v/axm_40c40d983216ae005ae479e32854ef2e8296024ebbdebe4443079459a1231a32"}}